Image semantic segmentation is intended to identify the image regions corresponding directly to objects in an image by labeling each pixel in the image to a semantic category. Contrary to the object recognition which merely detects the objects in the image, semantic segmentation assigns a category label to each pixel to indicate an object to which the pixel belongs. As such, semantic segmentation aims to assign a categorical label to every pixel in an image, which plays an important role in image analysis and self-driving systems.
Triplet loss seeks to minimize the distance between an anchor element and a positive element, both of which have the same identity, and seeks to maximize the distance between the anchor element and a negative element of a different identity. One advantage of triplet loss is that it tries to be less “greedy” than the contrastive loss (which considers pairwise examples). This is because triplet loss takes an anchor element and tries to bring positive elements closer while also pushing away negative elements. Triplet loss techniques have been used in conventional facial recognition systems, but have not been applied to image analysis in connection with self-driving and vehicle control systems.